Dataset Information

Single-Cell RNA-Seq Analysis Reveals Microenvironmental Infiltration of Plasma Cells and Hepatocytic Prognostic Markers in HCC With Cirrhosis.

ABSTRACT: The occurrence of hepatocellular carcinoma (HCC) related to liver cirrhosis is mostly accompanied by extensive immune infiltration. To reveal the infiltration immune cells landscape, single-cell RNA sequencing data from the healthy donor (HD), patients with liver cirrhosis (LC) and HCC were collected for analysis. By drawing a cell map and calculating the proportion of each cell type, total B cells were identified with a significant higher proportion in HCC (24.26%) than in LC (5.41%) and HD (5.82%), in which plasma cells account for 97.1% in HCC. While in HCC, TCGA datasets were taken for further investigation, and it was found that patients with lower proportion of plasma cells showed better prognosis. The pseudotime cell trajectory analysis of B cell population found that humoral immunity continuously changes during HD, LC and HCC, and humoral immune-related genes are highly expressed in the HCC stage. This suggests humoral immunity may play a key role in the development of LC-associated HCC. At the same time, single cell data of hepatocytes identified differentially expressed genes in HD/LC and LC/HCC groups, and a prognostic model constructed with six of the differential genes (FTCD, MARCKSL1, CXCL3, RGS5, KNG1, and S100A16) could classify HCC patients to two distinct risk groups (median survival time 2.46 years vs. 6.73 years, p < 0.001). Our study demonstrated the power of single-cell data analysis in dissecting tissues into infiltration and main body cells, it revealed the pivotal roles of humoral immunity infiltration in the landscape of HCC associated with cirrhosis, and the key tumor prognostic genes in hepatocytes themselves. These brought novel insights into studying microenvironment and tumor cells parallelly in cancer research. The interaction of both, rather than factors from one side may have caused tumorigenesis and progression.

SUBMITTER: Zhang S

PROVIDER: S-EPMC7667372 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Single-Cell RNA-Seq Analysis Reveals Microenvironmental Infiltration of Plasma Cells and Hepatocytic Prognostic Markers in HCC With Cirrhosis.

Zhang Siwen S Liu Zhenhao Z Wu Dan D Chen Lanming L Xie Lu L

Frontiers in oncology 20201102

The occurrence of hepatocellular carcinoma (HCC) related to liver cirrhosis is mostly accompanied by extensive immune infiltration. To reveal the infiltration immune cells landscape, single-cell RNA sequencing data from the healthy donor (HD), patients with liver cirrhosis (LC) and HCC were collected for analysis. By drawing a cell map and calculating the proportion of each cell type, total B cells were identified with a significant higher proportion in HCC (24.26%) than in LC (5.41%) and HD (5. ...[more]

PMID: 33224891

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Project description:BackgroundBreast cancer (BC) is a heterogeneous disease, with the ductal subtype exhibiting significant cellular diversity that influences prognosis and response to treatment. Single-cell RNA sequencing data from the GEO database were utilized in this study to investigate the underlying mechanisms of cellular heterogeneity and to identify potential prognostic markers and therapeutic targets.MethodsBioinformatics analysis was conducted using R packages to analyze the single-cell sequencing data. The presence of highly variable genes and differences in malignant potency within the same BC samples were examined. Differential gene expression and biological function between Type 1 and Type 2 ductal epithelial cells were identified. Lasso regression and Cox proportional hazards regression analyses were employed to identify genes associated with patient prognosis. Experimental validation was performed in vitro and in vivo to confirm the functional relevance of the identified genes.ResultsThe analysis revealed notable heterogeneity among BC cells, with the presence of highly variable genes and differences in malignant behavior within the same samples. Significant disparities in gene expression and biological function were identified between Type 1 and Type 2 ductal epithelial cells. Through regression analyses, CYP24A1 and TFPI2 were identified as pivotal genes associated with patient prognosis. Kaplan-Meier curves demonstrated their prognostic significance, and experimental validation confirmed their inhibitory effects on malignant behaviors of ductal BC cells.ConclusionThis study highlights the cellular heterogeneity in ductal subtype breast cancer and delineates the differential gene expressions and biological functions between Type 1 and Type 2 ductal epithelial cells. The genes CYP24A1 and TFPI2 emerged as promising prognostic markers and therapeutic targets, exhibiting inhibitory effects on BC cell malignancy in vitro and in vivo. These findings offer the potential for improved BC management and the development of targeted treatment strategies.

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the leading causes of cancer death in the world owing to limitations in its prognosis. The current prognosis approaches include radiological examination and detection of serum biomarkers, however, both have limited efficiency and are ineffective in early prognosis. Due to such limitations, we propose to use RNA-Seq data for evaluating putative higher accuracy biomarkers at the transcript level that could help in early prognosis.MethodsTo identify such potential transcript biomarkers, RNA-Seq data for healthy liver and various HCC cell models were subjected to five different machine learning algorithms: random forest, K-nearest neighbor, Naïve Bayes, support vector machine, and neural networks. Various metrics, namely sensitivity, specificity, MCC, informedness, and AUC-ROC (except for support vector machine) were evaluated. The algorithms that produced the highest values for all metrics were chosen to extract the top features that were subjected to recursive feature elimination. Through recursive feature elimination, the least number of features were obtained to differentiate between the healthy and HCC cell models.ResultsFrom the metrics used, it is demonstrated that the efficiency of the known protein biomarkers for HCC is comparatively lower than complete transcriptomics data. Among the different machine learning algorithms, random forest and support vector machine demonstrated the best performance. Using recursive feature elimination on top features of random forest and support vector machine three transcripts were selected that had an accuracy of 0.97 and kappa of 0.93. Of the three transcripts, two were protein coding (PARP2-202 and SPON2-203) and one was a non-coding transcript (CYREN-211). Lastly, we demonstrated that these three selected transcripts outperformed randomly taken three transcripts (15,000 combinations), hence were not chance findings, and could then be an interesting candidate for new HCC biomarker development.ConclusionUsing RNA-Seq data combined with machine learning approaches can aid in finding novel transcript biomarkers. The three biomarkers identified: PARP2-202, SPON2-203, and CYREN-211, presented the highest accuracy among all other transcripts in differentiating the healthy and HCC cell models. The machine learning pipeline developed in this study can be used for any RNA-Seq dataset to find novel transcript biomarkers. Code: www.github.com/rajinder4489/ML_biomarkers.

Dataset Information

Single-Cell RNA-Seq Analysis Reveals Microenvironmental Infiltration of Plasma Cells and Hepatocytic Prognostic Markers in HCC With Cirrhosis.

Publications

Single-Cell RNA-Seq Analysis Reveals Microenvironmental Infiltration of Plasma Cells and Hepatocytic Prognostic Markers in HCC With Cirrhosis.

Similar Datasets

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